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BCon 147: special topics

1 Project overiew

In this project, we will explore employee attrition and performance using the HR Analytics Employee Attrition & Performance dataset. The primary goal is to develop insights into the factors that contribute to employee attrition. By analyzing a range of factors, including demographic data, job satisfaction, work-life balance, and job role, we aim to help businesses identify key areas where they can improve employee retention.

2 Scenario

Imagine you are working as a data analyst for a mid-sized company that is experiencing high employee turnover, especially among high-performing employees. The company has been facing increased costs related to hiring and training new employees, and management is concerned about the negative impact on productivity and morale. The human resources (HR) team has collected historical employee data and now looks to you for actionable insights. They want to understand why employees are leaving and how to retain talent effectively.

Your task is to analyze the dataset and provide insights that will help HR prioritize retention strategies. These strategies could include interventions like revising compensation policies, improving job satisfaction, or focusing on work-life balance initiatives. The success of your analysis could lead to significant cost savings for the company and an increase in employee engagement and performance.

3 Understanding the source of data

The dataset used for this project provides information about employee demographics, performance metrics, and various satisfaction ratings. The dataset is particularly useful for exploring how factors such as job satisfaction, work-life balance, and training opportunities influence employee performance and attrition.

This dataset is well-suited for conducting in-depth analysis of employee performance and retention, enabling us to build predictive models that identify the key drivers of employee attrition. Additionally, we can assess the impact of various organizational factors, such as training and work-life balance, on both performance and retention outcomes.

## datatable function from DT package create an HTML widget display of the dataset
## install DT package if the package is not yet available in your R environment
read_excel("dataset/dataset-variable-description.xlsx") |> 
  DT::datatable()

4 Data wrangling

4.1 Data importation

Task 4.1. Merging dataset

• Import the two dataset Employee.csv and PerformanceRating.csv. Save the Employee.csv as employee_dta and PerformanceRating.csv as perf_rating_dta.

• Merge the two dataset using the left_join function from dplyr. Use the EmployeeID variable as the varible to join by.

• Save the merged dataset as hr_perf_dta.

## import the two dataset
employee_dta <- read_csv("dataset/Employee.csv")
perf_rating_dta <- read_csv("dataset/PerformanceRating.csv")

## merge employee_dta and perf_rating_dta
hr_perf_dta <- 
  employee_dta |> 
  left_join(perf_rating_dta, by = "EmployeeID")


## Use the datatable from DT package to display the merged dataset
DT::datatable(hr_perf_dta)

4.2 Data management

Task 4.2. Standardizing variable names

• Using the clean_names function from janitor package, standardize the variable names by using the recommended naming of variables.

• Save the renamed variables as hr_perf_dta to update the dataset.

## clean names using the janitor packages and save as hr_perf_dta
hr_perf_dta <- 
  hr_perf_dta |> 
  clean_names()

Recode data entries

• Create a new variable cat_education wherein education is 1 = No formal education; 2 = High school; 3 = Bachelor; 4 = Masters; 5 = Doctorate. Use the case_when function to accomplish this task.

• Similarly, create new variables cat_envi_sat, cat_job_sat, and cat_relation_sat for environment_satisfaction, job_satisfaction, and relationship_satisfaction, respectively. Re-code the values accordingly as 1 = Very dissatisfied; 2 = Dissatisfied; 3 = Neutral; 4 = Satisfied; and 5 = Very satisfied.

• Create new variables cat_work_life_balance, cat_self_rating, cat_manager_rating for work_life_balance, self_rating, and manager_rating, respectively. Re-code accordingly as 1 = Unacceptable; 2 = Needs improvement; 3 = Meets expectation; 4 = Exceeds expectation; and 5 = Above and beyond.

• Save all the changes in the hr_perf_dta.

## create cat_education


## create cat_envi_sat,  cat_job_sat, and cat_relation_sat


## create cat_work_life_balance, cat_self_rating, and cat_manager_rating


## print the updated hr_perf_dta using datatable function

5 Exploratory data analysis

5.1 Descriptive statistics of employee attrition

Task 5.1.1.Breakdown of attrition by key variables

• Select the variables attrition, job_role, department, age, salary, job_satisfaction, and work_life_balance. Save as attrition_key_var_dta.

• Compute and plot the attrition rate across job_role, department, and age, salary, job_satisfaction, and work_life_balance.

• Attrition rate across job_role has been done for you! You have the freedom to customize your plot accordingly. Show your creativity!

## calculating and plotting attrition rate
hr_perf_dta |> 
  group_by(job_role) |> 
  count(attrition) |> 
  mutate(pct_attrition = n / sum(n)) |> 
  ungroup() |> 
  mutate(job_role = reorder_within(job_role, pct_attrition, attrition)) |> 
  ggplot(aes(pct_attrition, job_role, fill = attrition)) +
  geom_col(position = "dodge", width = 0.8) +
  scale_y_reordered() +
  facet_wrap(~ attrition, scales = "free_y", ncol = 1) +
  labs(x = "Attrition rate",
       y = "Job role")

5.2 Identifying attrition key drivers using correlation analysis

5.3 Predictive modeling for attrition

5.4 Employee satisfaction and performance analysis

5.5 Analysis of compensation and turnover

5.6 Work-life balance and retention strategies

5.7 Recommendations for HR interventions